Hydra-Nav: Object Navigation via Adaptive Dual-Process Reasoning
Zixuan Wang, Huang Fang, Shaoan Wang, Yuanfei Luo, Heng Dong, Wei Li, Yiming Gan

TL;DR
Hydra-Nav is a novel vision-language model architecture that adaptively switches between deliberative and reactive reasoning systems, significantly improving object navigation success rates and efficiency across multiple benchmarks.
Contribution
It introduces Hydra-Nav, a unified VLM architecture with adaptive dual-process reasoning and a three-stage training curriculum for improved object navigation.
Findings
Achieves state-of-the-art results on HM3D, MP3D, and OVON benchmarks.
Outperforms previous methods by 11.1%, 17.4%, and 21.2%.
Adaptive reasoning improves search efficiency significantly.
Abstract
While large vision-language models (VLMs) show promise for object goal navigation, current methods still struggle with low success rates and inefficient localization of unseen objects--failures primarily attributed to weak temporal-spatial reasoning. Meanwhile, recent attempts to inject reasoning into VLM-based agents improve success rates but incur substantial computational overhead. To address both the ineffectiveness and inefficiency of existing approaches, we introduce Hydra-Nav, a unified VLM architecture that adaptively switches between a deliberative slow system for analyzing exploration history and formulating high-level plans, and a reactive fast system for efficient execution. We train Hydra-Nav through a three-stage curriculum: (i) spatial-action alignment to strengthen trajectory planning, (ii) memory-reasoning integration to enhance temporal-spatial reasoning over…
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Taxonomy
TopicsMultimodal Machine Learning Applications · Advanced Neural Network Applications · Reinforcement Learning in Robotics
